@inproceedings{
 title = {Multi-graph-view learning for complicated object classification},
 type = {inproceedings},
 year = {2015},
 pages = {3953-3959 (CORE Ranked A*)},
 volume = {2015-Janua},
 publisher = {International Joint Conferences on Artificial Intelligence},
 id = {5e9269c6-d5a7-3858-ad2a-ca9c2c05bed8},
 created = {2016-04-29T05:47:48.000Z},
 file_attached = {false},
 profile_id = {079852a8-52df-3ac8-a41c-8bebd97d6b2b},
 last_modified = {2022-04-10T12:11:22.332Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Wu2015c},
 folder_uuids = {f3b8cf54-f818-49eb-a899-33ac83c5e58d,2327f56c-ffc0-4246-bac0-b9fa6098ebfb},
 private_publication = {false},
 abstract = {In this paper, we propose to represent and classify complicated objects. In order to represent the objects, we propose a multi-graph-view model which uses graphs constructed from multiple graph-views to represent an object. In addition, a bag based multi-graph model is further used to relax labeling by only requiring one label for a bag of graphs, which represent one object. In order to learn classification models, we propose a multi-graph-view bag learning algorithm (MGVBL), which aims to explore subgraph features from multiple graphviews for learning. By enabling a joint regularization across multiple graph-views, and enforcing labeling constraints at the bag and graph levels, MGVBL is able to discover most effective subgraph features across all graph-views for learning. Experiments on real-world learning tasks demonstrate the performance of MGVBL for complicated object classification.},
 bibtype = {inproceedings},
 author = {Wu, Jia and Pan, Shirui and Zhu, Xingquan and Cai, Zhihua and Zhang, Chengqi},
 booktitle = {IJCAI International Joint Conference on Artificial Intelligence}
}

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